---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8339719029374202
    - name: Recall
      type: recall
      value: 0.8339719029374202
    - name: F1
      type: f1
      value: 0.8319571049551264
    - name: Precision
      type: precision
      value: 0.8325133593723552
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-patch16-224-finetuned-ind-17-imbalanced-aadhaarmask-new-parameter

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3507
- Accuracy: 0.8340
- Recall: 0.8340
- F1: 0.8320
- Precision: 0.8325

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| No log        | 0.9974 | 293  | 0.6168          | 0.7923   | 0.7923 | 0.7737 | 0.7684    |
| No log        | 1.9983 | 587  | 0.4599          | 0.8110   | 0.8110 | 0.8056 | 0.8085    |
| No log        | 2.9991 | 881  | 0.4305          | 0.8233   | 0.8233 | 0.8211 | 0.8250    |
| No log        | 4.0    | 1175 | 0.3966          | 0.8365   | 0.8365 | 0.8323 | 0.8452    |
| No log        | 4.9974 | 1468 | 0.4100          | 0.8221   | 0.8221 | 0.8195 | 0.8219    |
| No log        | 5.9983 | 1762 | 0.3890          | 0.8412   | 0.8412 | 0.8375 | 0.8466    |
| No log        | 6.9991 | 2056 | 0.3659          | 0.8357   | 0.8357 | 0.8335 | 0.8386    |
| No log        | 8.0    | 2350 | 0.3562          | 0.8395   | 0.8395 | 0.8379 | 0.8403    |
| No log        | 8.9974 | 2643 | 0.3613          | 0.8382   | 0.8382 | 0.8373 | 0.8391    |
| 0.4339        | 9.9745 | 2930 | 0.3405          | 0.8455   | 0.8455 | 0.8447 | 0.8467    |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.19.0
- Tokenizers 0.19.1